Aristotle (Greek philosopher), is the father of Science, who has defined the earliest formal study of logic (Logos). As well as, basic principles, and concepts of quite a few significant areas, such as physics, biology or psychology. He made the scientific discoveries throughout careful observations and logical interpretations. His theory has been exemplified by the Syllogistic Model (deductive argument, that has two premises and a conclusion). Aristotle was the first to catalogue and systematically study the rules of logical reasoning.’The question is whether logic is a subject matter to be studied (a science), or merely a method to be used by the various sciences. (This became a topic of dispute between the Stoics and Peripatetics)’.As part of Aristotle’s legacy, it has been determined, which deductions in ‘the figures example were possible; further including drawing the numbers of metatheoretical conclusions:No deduction has two negative premises.No deduction has two particular premises.A deduction with an affirmative conclusion must have two affirmative premises.A deduction with a negative conclusion must have one negative premise.A deduction with a universal conclusion must have two universal premises’.Deductions are only one of the two categories of argument, that has been recognised by Aristotle. The other exemplification – induction; a cognitive process, that initiates from particulars, and leads to their generalizations. An induction plays an immersive role in the Theory of scientific knowledge, named: ‘Posterior Analytics’.Based on the modern World observations, we can come to the conclusion, that majority of the total reasoning in science nowadays is formed by using an induction model. Observing the natural phenomena surrounding us leads researchers to question it, and begins to create scientific questions and to formulate a hypothesis.An alternative hypothesis enables researchers to test the predictions of a theory. In cases, when the predictions happen to be inaccurate, the theory needs to be refinement, as incorrect. ‘When we reason, we use relationships among propositions to push our knowledge beyond the limits of what we can experience directly’.The below illustration visualizes the types of reasoning during the research processes: Inductive, Deductive, Abductive, that I will be further expanding in more detail. Inductive reasoning is based on a string of specific observations (empirical data), to endorse the prospect of a more general conclusion; imagination, prior experience and knowledge; to testable theories, and then to the scientific laws.According to Kelley (2014), ‘the basic mode of inductive reasoning consists of drawing a universal conclusion about a class of things from premises about certain members of that class. In an inductive argument, the conclusion amplifies – it goes beyond – what the premises state. As a result, the truth of the premises does not guarantee the truth of the conclusion; there is some possibility, however small, that the conclusion is false. Inductive arguments have degrees of strength, depending not only on the relationship between premises and conclusion but also on a wider context of other available information’.As a weakness, we can consider the fact, that an inductive logic, never enables to fully manifest truth with 100% validity. But, most certainly, can strengthen the probability of a specific conclusion, by building on some more evidence (as per below case).An example of an inductive reasoning:Premise/Observation: All swans, I have seen are white.Conclusion: All swans must be white. (including ones I have never seen)The strength, of an induction, is the possibility of mining our data (to establish relationships or identify the patterns), and acknowledgement of a remarkable piece of information. From where, we can demonstrate the evidence of matters, as well as we can form the hard facts. Most of the mathematical discoveries, advanced from conclusions made with observations, and the process coming out of an inductive logic.In contrast to an inductive reasoning, deductive signs of progress from one or more general principles and advances to a specific and definite conclusion using the logic exclusively.Snieder and Larner (2009) recognise, that ‘the deductive approach follows the path of logic most closely. The reasoning starts with a theory and leads to a new hypothesis. This hypothesis is put to the test by confronting it with observations that either leads to a confirmation or a rejection of the hypothesis’. Whereas, Kelley (2014) describes, ‘the role of a deductive argument is to draw a conclusion that is contained implicitly in the premises. A deductive argument is either valid or invalid; there are no intermediate degrees of partial validity. If the argument is valid, then the conclusion follows necessarily from the premises’.An example of a deductive reasoning:Reason 1: All men are mortal.Reason 2: Aristotle is a man.Conclusion: Therefore, Aristotle is mortal too.The weakness, of deductive reasoning, is the fact, that the conclusion extracted from it, is fully indisputable (there is no possibility, of recognizing partly well-founded interpretations). As well as, that the conclusion does not append any new information as such; hence our initial data, already presented it transparently. Nor does the reasoning, coming from a limited experience, for example. As a strength factor, deductive reasoning is being fully compelling, when it is formed on a definition. The conclusion of it is logically reasonable, and rather undeniable. Its advantage would be reliability and a high degree of certainty. As well as the possibility, of shortened, limited time available to complete the research. The third method of reasoning, abduction, is defined as a process of selecting the hypothesis, that would best interpret the available affirmation. Abductive reasoning, as a third alternative, gains a victory over the other approaches disadvantages. By embracing a Pragmatist standpoint; developing the best predictions, that may or may not happen to be true. Bryman and Bell (2015) describe, that ‘in abductive approach, the research process starts with surprising facts or puzzles and the research process is devoted their explanation’. It appears also to be creative, and often intuitive, towards casual interpretations.Figure 2. presents a valid correlation, of all three reasoning sorts, and stressed out the iterative cycle in the Design Thinking, stimulating new ideas generation via abduction. Aliseda (2007) relates, ‘an abductive approach naturally leads to connections with theories of explanation and empirical progress in the philosophy of science, to computationally oriented theories of belief change in artificial intelligence, and to the philosophical position known as Pragmatism, proposed by Charles Peirce, to whom the term abduction owes its name’. ‘Philosophers, as well as psychologists, tend to agree that abduction is frequently employed in everyday reasoning’. One of a disadvantage could be a faulty diagnosis, in some cases of applying it into an Artificial Intelligence executable information systems. A conclusion in an abductive logic is the best, educated guess (the premises do not guarantee the conclusion, but rather an inference progressing to the best interpretation). Resuming, an abductive validation is an approach of distinguishing speculations, leading to a goal, often using intuitive power. It is definitely a driving force of creativity.Based on two professional research journals, I have carefully examined and deeply analyzed, the two most popular approaches of scientific reasoning included in those Research Project Reports. Based on Department of Computer Science, Lund University, Product-Focused Software Process Improvement Publication: ‘A qualitative survey of regression testing practices’, I distinguished, the inductive reasoning approach, towards developing new knowledge. Firstly, prior theoretical knowledge – existing regression models were carefully reviewed.Leading to the creation of the aim of this research, as the need for: ‘Regression testing practices in the industry have to be better understood, both for the industry itself and for the research community’ (p.2).Secondly, making real-life observations and new hypotheses suggestion (addresses validity issues), were backed up with a series of methods used in this research. Such: Qualitative Industry Survey.Focus Groups of 15 attendees.Online Questionnaire of 32 participants, used for validating the outcome purpose.Additionally, an analysis of the results from the developing new experience report, highlights that Zachman framework was used, based on what, how, where, who, when and why categories, although what, when, and how were the primary focus. ‘Zachman proposed that it might be used for developing new approaches to system development’ (p.10). ‘Everyone in the focus group agreed that it is better to test continuously than in large batches. A rule of thumb is to plan for as much test time as development time even when the project is delayed’ (p.14).Summarizing, a very important aspect of this research came out of the conclusion part. Building new knowledge element, stressed out the importance of taking into consideration the ‘good practices’, that were not specific to regression testing. However, are a management practice that becomes critical to regression testing as it constitutes a key part of the development project progress. This indicates that regression testing should not be addressed nor researched in isolation; rather it should be an important aspect of software testing practice and research to take into account’ (p.15).Based on a Computer Science and Security Journal: ‘A Novel Method for Quantitative Assessment of Software Quality’, I have identified the deductive reasoning approach, towards creating new knowledge.Initially, it has started with reviewing an existing theoretical knowledge/theories. Journal refers to: ‘more than 300 standards developed and maintained by more than 50 different organizations’ (p.509). Thus, recognizing that none of the currently in placed standard models, is fully efficient. In order to accomplish higher quality and improvement of: ‘performance for quality, productivity and customer satisfaction by the organizations’ (p.511) – new Theoretical Framework has been built, as per Figure 3. below.Furthermore, suggested new hypothesis and propositions, has been practically applied and tested, upon completing the Case Study. (pp.513-514). As the final result – creating new knowledge has been achieved, hence: ‘The case study validates the suitability and usefulness of the proposed model’ (p.515).When working on many different projects, that involved a lot of thinking outside the box. I have found, that mixing the approaches to reasoning, actually supports and immensely improves Innovation building aspect. Having highlighted that, I do not personally believe for a success factor, of using only one reasoning type, in order to achieve complex objectives. After being exposed to the experience of working over 10 years in a cross-sectors, and cross-functional, multinational environments; I know very well, that decision-making, upon having incomplete information and/or set of observations is uneasy and very challenging. Progressing further to the most probable interpretation requires mixing different reasoning approaches together, or flexing from one to another, when necessary. Allowing, for properly inspecting the Phenomena at hands. Investigating it from all the possible angles and levels, in order to formulate the most efficient problem resolutions, theories, accurate estimations based on limited data resources, or complex-multidisciplinary process flow mapping interpretations. Figure 4. visualizes, that a combination of the methods, can produce more innovative solutions, and allow to better find the Strategic Edge.Following the thoughts after reading an Interview on the subject of – ‘Design Thinking and How It Will Change Management Education’, by David Dunne and Roger Martin, dean of the Rotman School of Management, University of Toronto. I can see an opportunity in applying ‘design thinking’, when approaching design problems, in relation to advancing towards resolving managerial problems. Martin ‘saw that this is what great business leaders do. They enter some kind of constrained environment where they want to do something that is near impossible. They have to figure it out by thinking differently from anybody else. Following the designers’ approach, who can solve the most wicked problems do it through collaborative integrative thinking, using abductive logic, which means the logic of what might be’. Scientific research is highly influenced by logical positivism, and also by scientific method application. Presuming, that new knowledge, can only be generated by evolving and testing the theory, based on either deduction or induction. Inductive reasoning requires deduction to determine segments, quantify them, and create a structure inside the holistic paradigms. Deduction clarifies unity and orderliness within the systems.Dubois and Gadde (2002), present some basic cornerstones of ‘systematic combining’ approach in their Journal of Business Research. ‘Systematic combining is a process where theoretical framework, empirical fieldwork, and case analysis evolve simultaneously, and it is particularly useful for the development of new theories’.Further elaboration on this particular case study, explains that the most important attribute of this approach, is an ongoing movement (continuous iterations), between the two: model and empirical worlds. This sort of empowerment and the way of thought is something, that I am particularly impressed with. And, I strongly believe, by using the mixture of the methods at the same time, combined together, collaborative integrative thinking, and an abductive logic. Researchers would have had endless opportunities, that would not be needed to be limited, by selecting only one path (induction) or the other one (deduction). By pushing the boundaries, experimenting, expanding; conventions and assumptions can be challenged better. Resulting in achieving significant successes, while designing fresh, new ideas, or theories, that much needed in every sector of life.